Facilitating safe discharge through predicting disease progression in moderate COVID-19: a prospective cohort study to develop and validate a clinical prediction model in resource-limited settings Article Swipe
Arjun Chandna
,
Raman Mahajan
,
Priyanka Gautam
,
Lazaro Mwandigha
,
Karthik Gunasekaran
,
Divendu Bhusan
,
Arthur T L Cheung
,
Nicholas Day
,
Sabine Dittrich
,
Arjen M. Dondorp
,
Tulasi Geevar
,
Srinivasa R Ghattamaneni
,
Samreen Hussain
,
Carolina Jimenez
,
Rohini Karthikeyan
,
Sanjeev Kumar
,
Shiril Kumar
,
Vikash Kumar
,
Debasree Kundu
,
Ankita Lakshmanan
,
Abi Manesh
,
Chonticha Menggred
,
Mahesh Moorthy
,
Jennifer Osborn
,
Melissa Richard‐Greenblatt
,
Sadhana Sharma
,
Veena Singh
,
Vikash Kumar Singh
,
Javvad Suri
,
Shuichi Suzuki
,
Jaruwan Tubprasert
,
Paul Turner
,
Annavi Marie G. Villanueva
,
Naomi Waithira
,
Pragya Kumar
,
George M. Varghese
,
Constantinos Koshiaris
,
Yoel Lubell
,
Sakib Burza
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.12.02.21267170
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.12.02.21267170
Background In locations where few people have received COVID-19 vaccines, health systems remain vulnerable to surges in SARS-CoV-2 infections. Tools to identify patients suitable for community-based management are urgently needed. Methods We prospectively recruited adults presenting to two hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 in order to develop and validate a clinical prediction model to rule-out progression to supplemental oxygen requirement. The primary outcome was defined as any of the following: SpO 2 < 94%; respiratory rate > 30 bpm; SpO 2 /FiO 2 < 400; or death. We specified a priori that each model would contain three clinical parameters (age, sex and SpO 2 ) and one of seven shortlisted biochemical biomarkers measurable using near-patient tests (CRP, D-dimer, IL-6, NLR, PCT, sTREM-1 or suPAR), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration and clinical utility of the models in a temporal external validation cohort. Findings 426 participants were recruited, of whom 89 (21·0%) met the primary outcome. 257 participants comprised the development cohort and 166 comprised the validation cohort. The three models containing NLR, suPAR or IL-6 demonstrated promising discrimination (c-statistics: 0·72 to 0·74) and calibration (calibration slopes: 1·01 to 1·05) in the validation cohort, and provided greater utility than a model containing the clinical parameters alone. Interpretation We present three clinical prediction models that could help clinicians identify patients with moderate COVID-19 suitable for community-based management. The models are readily implementable and of particular relevance for locations with limited resources. Funding Médecins Sans Frontières, India. RESEARCH IN CONTEXT Evidence before this study A living systematic review by Wynants et al. identified 137 COVID-19 prediction models, 47 of which were derived to predict whether patients with COVID-19 will have an adverse outcome. Most lacked external validation, relied on retrospective data, did not focus on patients with moderate disease, were at high risk of bias, and were not practical for use in resource-limited settings. To identify promising biochemical biomarkers which may have been evaluated independently of a prediction model and therefore not captured by this review, we searched PubMed on 1 June 2020 using synonyms of “SARS-CoV-2” AND [“biomarker” OR “prognosis”]. We identified 1,214 studies evaluating biochemical biomarkers of potential value in the prognostication of COVID-19 illness. In consultation with FIND (Geneva, Switzerland) we shortlisted seven candidates for evaluation in this study, all of which are measurable using near-patient tests which are either currently available or in late-stage development. Added value of this study We followed the TRIPOD guidelines to develop and validate three promising clinical prediction models to help clinicians identify which patients presenting with moderate COVID-19 can be safely managed in the community. Each model contains three easily ascertained clinical parameters (age, sex, and SpO 2 ) and one biochemical biomarker (NLR, suPAR or IL-6), and would be practical for implementation in high-patient-throughput low resource settings. The models showed promising discrimination and calibration in the validation cohort. The inclusion of a biomarker test improved prognostication compared to a model containing the clinical parameters alone, and extended the range of contexts in which such a tool might provide utility to include situations when bed pressures are less critical, for example at earlier points in a COVID-19 surge. Implications of all the available evidence Prognostic models should be developed for clearly-defined clinical use-cases. We report the development and temporal validation of three clinical prediction models to rule-out progression to supplemental oxygen requirement amongst patients presenting with moderate COVID-19. The models are readily implementable and should prove useful in triage and resource allocation. We provide our full models to enable independent validation.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.12.02.21267170
- https://www.medrxiv.org/content/medrxiv/early/2021/12/07/2021.12.02.21267170.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200333168
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4200333168Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2021.12.02.21267170Digital Object Identifier
- Title
-
Facilitating safe discharge through predicting disease progression in moderate COVID-19: a prospective cohort study to develop and validate a clinical prediction model in resource-limited settingsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
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2021-12-07Full publication date if available
- Authors
-
Arjun Chandna, Raman Mahajan, Priyanka Gautam, Lazaro Mwandigha, Karthik Gunasekaran, Divendu Bhusan, Arthur T L Cheung, Nicholas Day, Sabine Dittrich, Arjen M. Dondorp, Tulasi Geevar, Srinivasa R Ghattamaneni, Samreen Hussain, Carolina Jimenez, Rohini Karthikeyan, Sanjeev Kumar, Shiril Kumar, Vikash Kumar, Debasree Kundu, Ankita Lakshmanan, Abi Manesh, Chonticha Menggred, Mahesh Moorthy, Jennifer Osborn, Melissa Richard‐Greenblatt, Sadhana Sharma, Veena Singh, Vikash Kumar Singh, Javvad Suri, Shuichi Suzuki, Jaruwan Tubprasert, Paul Turner, Annavi Marie G. Villanueva, Naomi Waithira, Pragya Kumar, George M. Varghese, Constantinos Koshiaris, Yoel Lubell, Sakib BurzaList of authors in order
- Landing page
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https://doi.org/10.1101/2021.12.02.21267170Publisher landing page
- PDF URL
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https://www.medrxiv.org/content/medrxiv/early/2021/12/07/2021.12.02.21267170.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://www.medrxiv.org/content/medrxiv/early/2021/12/07/2021.12.02.21267170.full.pdfDirect OA link when available
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Medicine, Cohort, Clinical trial, Prospective cohort study, Cohort study, Vital signs, Community-acquired pneumonia, Disease, Emergency medicine, Intensive care medicine, Internal medicine, Pneumonia, SurgeryTop concepts (fields/topics) attached by OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.94%; | 78 |
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| abstract_inverted_index.oxygen | 63, 570 |
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| abstract_inverted_index.points | 533 |
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| abstract_inverted_index.Findings | 155 |
| abstract_inverted_index.RESEARCH | 257 |
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| abstract_inverted_index.contains | 447 |
| abstract_inverted_index.contexts | 511 |
| abstract_inverted_index.disease, | 308 |
| abstract_inverted_index.evidence | 543 |
| abstract_inverted_index.extended | 507 |
| abstract_inverted_index.external | 152, 295 |
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| abstract_inverted_index.identify | 22, 229, 325, 431 |
| abstract_inverted_index.illness. | 376 |
| abstract_inverted_index.improved | 495 |
| abstract_inverted_index.moderate | 43, 232, 307, 436, 576 |
| abstract_inverted_index.outcome. | 167, 292 |
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| abstract_inverted_index.resource | 476, 590 |
| abstract_inverted_index.rule-out | 59, 566 |
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| abstract_inverted_index.synonyms | 354 |
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| abstract_inverted_index.urgently | 29 |
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| abstract_inverted_index.Médecins | 253 |
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| abstract_inverted_index.biomarker | 462, 493 |
| abstract_inverted_index.comprised | 170, 176 |
| abstract_inverted_index.critical, | 528 |
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| abstract_inverted_index.developed | 548 |
| abstract_inverted_index.evaluated | 140, 333 |
| abstract_inverted_index.hospitals | 39 |
| abstract_inverted_index.inclusion | 490 |
| abstract_inverted_index.locations | 3, 248 |
| abstract_inverted_index.potential | 369 |
| abstract_inverted_index.practical | 318, 470 |
| abstract_inverted_index.pressures | 525 |
| abstract_inverted_index.promising | 189, 326, 424, 481 |
| abstract_inverted_index.recruited | 34 |
| abstract_inverted_index.relevance | 246 |
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| abstract_inverted_index.specified | 93 |
| abstract_inverted_index.therefore | 340 |
| abstract_inverted_index.vaccines, | 10 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Prognostic | 544 |
| abstract_inverted_index.SARS-CoV-2 | 18 |
| abstract_inverted_index.biomarkers | 116, 328, 367 |
| abstract_inverted_index.candidates | 386 |
| abstract_inverted_index.clinicians | 228, 430 |
| abstract_inverted_index.community. | 444 |
| abstract_inverted_index.containing | 183, 213, 501 |
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| abstract_inverted_index.following: | 74 |
| abstract_inverted_index.guidelines | 418 |
| abstract_inverted_index.identified | 272, 362 |
| abstract_inverted_index.late-stage | 407 |
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| abstract_inverted_index.parameters | 103, 216, 452, 504 |
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| abstract_inverted_index.presenting | 36, 434, 574 |
| abstract_inverted_index.recruited, | 159 |
| abstract_inverted_index.resources. | 251 |
| abstract_inverted_index.situations | 522 |
| abstract_inverted_index.systematic | 266 |
| abstract_inverted_index.use-cases. | 552 |
| abstract_inverted_index.validation | 153, 178, 204, 487, 559 |
| abstract_inverted_index.vulnerable | 14 |
| abstract_inverted_index.allocation. | 591 |
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| abstract_inverted_index.calibration | 142, 196, 484 |
| abstract_inverted_index.development | 172, 556 |
| abstract_inverted_index.independent | 599 |
| abstract_inverted_index.infections. | 19 |
| abstract_inverted_index.management. | 237 |
| abstract_inverted_index.progression | 60, 567 |
| abstract_inverted_index.requirement | 571 |
| abstract_inverted_index.respiratory | 79 |
| abstract_inverted_index.shortlisted | 114, 384 |
| abstract_inverted_index.validation, | 296 |
| abstract_inverted_index.validation. | 600 |
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| abstract_inverted_index.Frontières, | 255 |
| abstract_inverted_index.Implications | 538 |
| abstract_inverted_index.Switzerland) | 382 |
| abstract_inverted_index.consultation | 378 |
| abstract_inverted_index.demonstrated | 188 |
| abstract_inverted_index.development. | 408 |
| abstract_inverted_index.near-patient | 119, 398 |
| abstract_inverted_index.participants | 157, 169 |
| abstract_inverted_index.requirement. | 64 |
| abstract_inverted_index.supplemental | 62, 569 |
| abstract_inverted_index.implementable | 242, 582 |
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| abstract_inverted_index.prospectively | 33 |
| abstract_inverted_index.retrospective | 299 |
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| abstract_inverted_index.implementation | 472 |
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| abstract_inverted_index.community-based | 26, 236 |
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| abstract_inverted_index.“prognosis”]. | 360 |
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| corresponding_author_ids | https://openalex.org/A5052282267, https://openalex.org/A5074357118 |
| countries_distinct_count | 7 |
| institutions_distinct_count | 39 |
| corresponding_institution_ids | https://openalex.org/I2799599516, https://openalex.org/I40120149, https://openalex.org/I4210089966, https://openalex.org/I4210116389 |
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| citation_normalized_percentile.value | 0.44148148 |
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| citation_normalized_percentile.is_in_top_10_percent | False |